Compressive sensing based data collection in VANETs

Abstract

Vehicular ad hoc networks (VANETs) are emerging as an indispensable platform to collect vehicular sensor data, which can be applied to improve traffic efficiency and support numerous promising commercial applications. However, it is challenging to efficiently collect these data without overloading the network. In this paper, a novel scheme, compressive sensing based data collection (CS-DC), is proposed to efficiently collect spatially correlated data in VANETs. CS-DC is able to efficiently reduce communication overhead with low computation and less communication control. To achieve high cluster stability in CS-DC, the distance and mobility based clustering protocol (DIMOC) is proposed to support reliable data transmissions among neighboring nodes. Furthermore, the compressive sensing (CS) theory is applied to efficiently compress in-network data and accurately recover original data. Simulation results show that the CS-DC scheme significantly improves the efficiency, scalability and reliability of data collection in VANETs.